Modern server computing devices are often physically configured in a manner to promote the installation and maintenance of multiple such server computing devices within a confined space, such as a rack. Multiple racks of server computing devices can then be housed in a dedicated facility, commonly referred to as a “data center”. By aggregating computing resources together, datacenters can provide efficiencies of scale while simultaneously providing increased computational ability and availability. For example, datacenters can comprise thousands of computing devices whose combined processing capabilities can be divided and shared in a myriad of ways, thereby enabling entities to access a far greater amount of processing capability, for less cost, than if such entities were to purchase the computing devices themselves. As another example, datacenters can implement redundancy mechanisms that can be prohibitively expensive on individual basis, but can provide inexpensive risk reduction when their cost is spread out across all of the users of a data center. Such redundancy mechanisms can include the partitioning of computing devices, within the data center, into failure domains that can comprise physically distinct locations, independent network connections and sources of power, and other like attributes such that most potential failures should only affect a single failure domain.
Often, to maintain reliability, redundant copies of data are maintained at multiple ones of such failure domains within a data center, or across multiple, physically distinct, datacenters. Such data sets, however, can be sufficiently large that communication of the data of such data sets across networks can be time-consuming and costly. As a result, redundancy is often achieved at the cost of network resource consumption and, consequently, performance degradation. Additionally, redundancy mechanisms often entail the performance of mathematical operations on the data that can be computationally expensive. Consequently, redundancy can, in addition to increasing network resource consumption, also increase processing capability utilization. Since both network resources and processing capability can be supported by expensive computational hardware, and other like capital investments, efficient utilization thereof, while maintaining redundancy, can be desirable.